Drug sensitivity prediction for individual patients is a significant challenge for precision medicine. Current modeling approaches consider prediction of a single feature of the drug response curve such as Area Under the Curve or IC50. However, the single feature summary of the dose response curve does not provide the entire drug sensitivity profile as some features vary systematically with cell lines while others with drugs. The overall goal of this proposal is to design a mathematical and computational framework for dose response curve prediction based on target response curves and genetic characterizations. For individual patients, the problem is formulated as functional prediction from functional predictors. The functional predictors refer to the dose response of specific targets inhibited by the drug that can be obtained from chemical databases and drug kinase activity studies. To achieve our goal, we propose three specific aims: In Aim 1, we will design a Bayesian framework for estimating the significant drivers of a disease along with the design of a model for single and combination drug response prediction. In Aim 2, we will enhance the model by incorporating non-functional predictors in the form of genetic characterizations and develop a joint model to predict dose response curves with both genetic characteristics as well as target response curves as inputs. We propose to develop an efficient way of searching such extremely high dimensional predictor space. In Aim 3, we will develop a hybrid prediction mechanism that combines inferentially motivated model-based techniques and computationally efficient machine learning techniques to improve predictions as well as obtain significant predictors simultaneously. The proposed framework is appropriate for modeling and analyzing cellular response to single or combination of perturbation agents and the successful completion of the aims will allow us to design personalized therapy along with increased understanding of functional response of cells to external perturbations.